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Clustering Cancer Tumours using Unsupervised Deep Learning Techniques

The modern technology of DNA microarrays has made high-dimensional genomic data available for large-scale analysis. This thesis investigates how unsupervised deep learning techniques may be used as a class discovery method analysing cancer tumour data. Furthermore, the possibility of inferring which genes most strongly contribute in the differentiation of cancer types is discussed.
Gene expression data from The Cancer Genome Atlas of 10 different cancer tumour types are analysed. A deep autoencoder network clearly separates cancer tumours as well as known subtypes of tumours already in 2-dimensions. The results are compared with other dimensionality reduction methods like principal component analysis.

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BibTeX @mastersthesis{Lilja2016,author={Lilja, Oskar},title={Clustering Cancer Tumours using Unsupervised Deep Learning Techniques },abstract={The modern technology of DNA microarrays has made high-dimensional genomic data available for large-scale analysis. This thesis investigates how unsupervised deep learning techniques may be used as a class discovery method analysing cancer tumour data. Furthermore, the possibility of inferring which genes most strongly contribute in the differentiation of cancer types is discussed.
Gene expression data from The Cancer Genome Atlas of 10 different cancer tumour types are analysed. A deep autoencoder network clearly separates cancer tumours as well as known subtypes of tumours already in 2-dimensions. The results are compared with other dimensionality reduction methods like principal component analysis. },publisher={Institutionen för matematiska vetenskaper, Chalmers tekniska högskola},place={Göteborg},year={2016},}

RefWorks RT GenericSR PrintID 242825A1 Lilja, OskarT1 Clustering Cancer Tumours using Unsupervised Deep Learning Techniques YR 2016AB The modern technology of DNA microarrays has made high-dimensional genomic data available for large-scale analysis. This thesis investigates how unsupervised deep learning techniques may be used as a class discovery method analysing cancer tumour data. Furthermore, the possibility of inferring which genes most strongly contribute in the differentiation of cancer types is discussed.
Gene expression data from The Cancer Genome Atlas of 10 different cancer tumour types are analysed. A deep autoencoder network clearly separates cancer tumours as well as known subtypes of tumours already in 2-dimensions. The results are compared with other dimensionality reduction methods like principal component analysis. PB Institutionen för matematiska vetenskaper, Chalmers tekniska högskola,LA engOL 30